research-article

DQ-DASH: A Queuing Theory Approach to Distributed Adaptive Video Streaming

Authors:

Abdelhak Bentaleb,

Praveen Kumar Yadav,

Roger ZimmermannAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 1

Article No.: 4, Pages 1 - 24

https://doi.org/10.1145/3371040

Published: 04 March 2020 Publication History

Abstract

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a Distributed Queuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M^x/D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

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Cited By

Bentaleb ALim MAkcay MBegen AZimmermann R(2024)Bitrate Adaptation and Guidance With Meta Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.337656023:11(10378-10392)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3376560
Nguyen NLuu LVo PNguyen SDo CNguyen N(2023)Reinforcement learning - based adaptation and scheduling methods for multi-source DASHComputer Science and Information Systems10.2298/CSIS220927055N20:1(157-173)Online publication date: 2023
https://doi.org/10.2298/CSIS220927055N
Ansari AWang MZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)iStream Player: A Versatile Video Player FrameworkProceedings of the 33rd Workshop on Network and Operating System Support for Digital Audio and Video10.1145/3592473.3592569(65-71)Online publication date: 7-Jun-2023
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Index Terms

DQ-DASH: A Queuing Theory Approach to Distributed Adaptive Video Streaming
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 1

February 2020

363 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3384216

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2020 ACM.

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Publication History

Published: 04 March 2020

Accepted: 01 October 2019

Revised: 01 October 2019

Received: 01 November 2018

Published in TOMM Volume 16, Issue 1

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https://doi.org/10.1109/TMC.2024.3376560
Nguyen NLuu LVo PNguyen SDo CNguyen N(2023)Reinforcement learning - based adaptation and scheduling methods for multi-source DASHComputer Science and Information Systems10.2298/CSIS220927055N20:1(157-173)Online publication date: 2023
https://doi.org/10.2298/CSIS220927055N
Ansari AWang MZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)iStream Player: A Versatile Video Player FrameworkProceedings of the 33rd Workshop on Network and Operating System Support for Digital Audio and Video10.1145/3592473.3592569(65-71)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3592473.3592569
Bentaleb AFarahani RTashtarian FHellwagner HZimmermann RTimmerer CBegen AGrois DShoham TGiladi A(2023)Which CDN to Download From? A Client and Server StrategiesProceedings of the 2nd Mile-High Video Conference10.1145/3588444.3591030(135-136)Online publication date: 7-May-2023
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Karagkioules TPaschos GLiakopoulos NFiandrotti ATsilimantos DCagnazzo M(2022)Online Learning for Adaptive Video Streaming in Mobile NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346081918:1(1-22)Online publication date: 27-Jan-2022
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